"""simple docstring"""


import json
import os
import subprocess
import unittest
from ast import literal_eval

import pytest
from parameterized import parameterized, parameterized_class

from . import is_sagemaker_available


if is_sagemaker_available():
	from sagemaker import Session, TrainingJobAnalytics
	from sagemaker.huggingface import HuggingFace






@pytest.mark.skipif(
    literal_eval(os.getenv('''TEST_SAGEMAKER''' , '''False'''      )      ) is not True , reason='''Skipping test because should only be run when releasing minor transformers version''' , )
@pytest.mark.usefixtures('''sm_env'''      )
@parameterized_class(
    [
        {
            '''framework''': '''pytorch''',
            '''script''': '''run_glue.py''',
            '''model_name_or_path''': '''distilbert-base-cased''',
            '''instance_type''': '''ml.p3.16xlarge''',
            '''results''': {'''train_runtime''': 6_5_0, '''eval_accuracy''': 0.7, '''eval_loss''': 0.6},
        },
        {
            '''framework''': '''pytorch''',
            '''script''': '''run_ddp.py''',
            '''model_name_or_path''': '''distilbert-base-cased''',
            '''instance_type''': '''ml.p3.16xlarge''',
            '''results''': {'''train_runtime''': 6_0_0, '''eval_accuracy''': 0.7, '''eval_loss''': 0.6},
        },
        {
            '''framework''': '''tensorflow''',
            '''script''': '''run_tf_dist.py''',
            '''model_name_or_path''': '''distilbert-base-cased''',
            '''instance_type''': '''ml.p3.16xlarge''',
            '''results''': {'''train_runtime''': 6_0_0, '''eval_accuracy''': 0.6, '''eval_loss''': 0.7},
        },
    ]      )
class   __UpperCAmelCase		(      unittest.TestCase      ):

				def 						_a      ( self ):
										if self.framework == "pytorch":
																subprocess.run(
																    F'''cp ./examples/pytorch/text-classification/run_glue.py {self.env.test_path}/run_glue.py'''.split()      ,					encoding="utf-8"      ,					check=_lowerCamelCase      ,					)
										assert hasattr(self      ,					"env" )

				def 						_a      ( self      ,					_lowerCamelCase ):
										lowerCamelCase__   =F'''{self.env.base_job_name}-{instance_count}-{'ddp' if 'ddp' in self.script else 'smd'}'''
										# distributed data settings
										lowerCamelCase__   ={"smdistributed": {"dataparallel": {"enabled": True}}} if self.script != "run_ddp.py" else None

										# creates estimator
										return HuggingFace(
										    entry_point=self.script      ,					source_dir=self.env.test_path      ,					role=self.env.role      ,					image_uri=self.env.image_uri      ,					base_job_name=_lowerCamelCase      ,					instance_count=_lowerCamelCase      ,					instance_type=self.instance_type      ,					debugger_hook_config=_lowerCamelCase      ,					hyperparameters={**self.env.distributed_hyperparameters, "model_name_or_path": self.model_name_or_path}      ,					metric_definitions=self.env.metric_definitions      ,					distribution=_lowerCamelCase      ,					py_version="py36"      ,					)

				def 						_a      ( self      ,					_lowerCamelCase ):
										TrainingJobAnalytics(_lowerCamelCase ).export_csv(F'''{self.env.test_path}/{job_name}_metrics.csv''' )






				@parameterized.expand([(2,)] )
				def 						_a      ( self      ,					_lowerCamelCase ):
										# create estimator
										lowerCamelCase__   =self.create_estimator(_lowerCamelCase )

										# run training
										estimator.fit()

										# result dataframe
										lowerCamelCase__   =TrainingJobAnalytics(estimator.latest_training_job.name ).dataframe()

										# extract kpis
										lowerCamelCase__   =list(result_metrics_df[result_metrics_df.metric_name == "eval_accuracy"]["value"] )
										lowerCamelCase__   =list(result_metrics_df[result_metrics_df.metric_name == "eval_loss"]["value"] )
										# get train time from SageMaker job, this includes starting, preprocessing, stopping
										lowerCamelCase__   =(
										    Session().describe_training_job(estimator.latest_training_job.name ).get("TrainingTimeInSeconds"      ,					999999 )
										)

										# assert kpis
										assert train_runtime <= self.results["train_runtime"]
										assert all(t >= self.results["eval_accuracy"] for t in eval_accuracy )
										assert all(t <= self.results["eval_loss"] for t in eval_loss )

										# dump tests result into json file to share in PR
										with open(F'''{estimator.latest_training_job.name}.json'''      ,					"w" ) as outfile:
																json.dump({"train_time": train_runtime, "eval_accuracy": eval_accuracy, "eval_loss": eval_loss}      ,					_lowerCamelCase )



"""simple docstring"""


def      lowerCamelCase_					(   __lowerCAmelCase  ,       __lowerCAmelCase							)				->   float:




						'''simple docstring'''



						return price * (1 + tax_rate)


if __name__ == "__main__":
	print(F'''{price_plus_tax(100, 0.25) = }''')
	print(F'''{price_plus_tax(125.50, 0.05) = }''')


